Improving computer vision for plant pathology through advanced training techniques

IF 2.4 3区 生物学 Q2 PLANT SCIENCES
Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks
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引用次数: 0

Abstract

Premise

This study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, Theobroma cacao.

Methods

Despite recent stagnation in accuracy improvements in computer vision for image classification, our research demonstrates significant advancements in performance through semi-supervised learning, specialised loss functions, and the inclusion of a non-cocoa class.

Results

Semi-supervised learning reduced overfitting and enhanced generalisability, particularly for subtle symptoms. The non-cocoa class exposed models to a broad range of relevant features, significantly improving model robustness and performance in difficult cases. Grad-CAM for qualitative assessment provided valuable insights into model behaviour, highlighting cases of overfitting missed by summary statistics. We also describe dynamic focal loss, a novel loss function that uses an empirical measure of difficulty to weight each image. Our results suggest that while PhytNet shows promise in terms of computational efficiency and superior handling of difficult images, ResNet18 with semi-supervised learning and dynamic focal loss emerged as the strongest contender for real-world deployment.

Discussion

This research underscores the potential of semi-supervised learning and advanced loss functions in enhancing the applicability of deep learning models in agricultural disease management. It also presents a new high-quality benchmark dataset of 7220 images of diseased and healthy cocoa trees, offering a much greater and more realistic challenge than the Plan Village dataset.

Abstract Image

通过先进的训练技术提高植物病理学的计算机视觉
本研究探讨了先进的训练技术,以提高卷积神经网络在可可疾病检测中的性能。尽管最近在计算机视觉图像分类的准确性提高方面停滞不前,但我们的研究表明,通过半监督学习、专门的损失函数和包含非可可类,在性能方面取得了重大进展。结果半监督学习减少了过拟合,提高了普遍性,特别是对细微症状。非可可类将模型暴露在广泛的相关特征中,显著提高了模型在困难情况下的鲁棒性和性能。用于定性评估的Grad-CAM为模型行为提供了有价值的见解,突出了汇总统计错过的过拟合案例。我们还描述了动态焦点损失,这是一种新的损失函数,它使用困难的经验度量来加权每个图像。我们的研究结果表明,虽然PhytNet在计算效率和对困难图像的卓越处理方面表现出了希望,但具有半监督学习和动态焦点损失的ResNet18成为现实世界部署的最强竞争者。本研究强调了半监督学习和高级损失函数在增强深度学习模型在农业疾病管理中的适用性方面的潜力。它还提供了一个新的高质量基准数据集,包含7220张患病和健康的可可树图像,比Plan Village数据集提供了更大、更现实的挑战。
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来源期刊
CiteScore
7.30
自引率
0.00%
发文量
50
审稿时长
12 weeks
期刊介绍: Applications in Plant Sciences (APPS) is a monthly, peer-reviewed, open access journal promoting the rapid dissemination of newly developed, innovative tools and protocols in all areas of the plant sciences, including genetics, structure, function, development, evolution, systematics, and ecology. Given the rapid progress today in technology and its application in the plant sciences, the goal of APPS is to foster communication within the plant science community to advance scientific research. APPS is a publication of the Botanical Society of America, originating in 2009 as the American Journal of Botany''s online-only section, AJB Primer Notes & Protocols in the Plant Sciences. APPS publishes the following types of articles: (1) Protocol Notes describe new methods and technological advancements; (2) Genomic Resources Articles characterize the development and demonstrate the usefulness of newly developed genomic resources, including transcriptomes; (3) Software Notes detail new software applications; (4) Application Articles illustrate the application of a new protocol, method, or software application within the context of a larger study; (5) Review Articles evaluate available techniques, methods, or protocols; (6) Primer Notes report novel genetic markers with evidence of wide applicability.
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